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|  Mitch Suzuki
Doctor of Science
NA Local time: 16:30 JST (GMT+9)
Native in: Japanese  | |
Doctor of Science | Freelancer,  Verified member | | Translation | | Specializes in: | | Computers: Systems, Networks | Computers (general) | | IT (Information Technology) | Electronics / Elect Eng | | Medical: Instruments | Computers: Software | | Patents | Physics | | Science (general) | Engineering (general) |
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| | Questions answered: 96, Questions asked: 0 Easy / 2 PRO, PRO-level points: 198 | 0 projects entered
| | 0 entries | Sample translations submitted: 1| English to Japanese: Bayesian network classifiers ベイジアンネットワーク識別器 | Source text - English
Bayesian network classifiers
N. Friedman, D. Geiger,
and M. Goldszmidt
Machine Learning 29:131--163, 1997.
Abstract
Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classifiers such as C4.5.
This fact raises the question of whether a classifier with less restrictive assumptions can perform even better.
In this paper we evaluate approaches for inducing classifiers from data, based on the theory of learning Bayesian networks.
Bayesian networks are factored representations of probability distributions that generalize the naive Bayesian classifier and explicitly represent statements about independence.
Among these approaches we single out a method we call Tree Augmented Naive Bayes (TAN), which outperforms naive Bayes, yet at the same time maintains the computational simplicity (no search involved) and robustness that are characteristic of naive Bayes.
We experimentally tested these approaches, using benchmark problems from the University of California at Irvine repository, and compared them to C4.5, naive Bayes, and wrapper-based feature selection methods.
| Translation - Japanese
ベイジアンネットワーク識別器
N. フリードマン, D. ゲイジャー,
M. ゴールドスミス
機械学習 29:131--163, 1997.
抄録
単純ベイズ法と呼ばれる識別器は、特徴成分間に独立性を仮定するという驚くほど単純な識別器であるにもかかわらず、C4.5のような最新の識別器と同程度の性能を持っていることが最近の教師付き学習の分野の研究からわかってきた。
したがって、この仮定をゆるめたときこの識別器がさらに良い性能をもたらすのではないかという質問が当然でてくる。
この論文では、ベイジアンネットワークの学習理論に基づいて学習データから識別器を生成するいくつかの方法について評価をする。
ベイジアンネットワークとは、単純ベイズ識別器を一般化し、独立性に関する記述を明瞭に表現した、因子分解された形の確率分布の表現方法である。
我々は、特に木型の強化された単純ベイズ法(TAN)と呼ばれる方法について考察していくが、この方法は、単純ベイズ法より性能が勝るだけでなく、探索が絡まないので数値計算上簡単であること、単純ベイズと同様に頑健であることなどの利点を兼ね備えている。
これらの方法について、カリフォルニア大学アーバインのリポジトリからの標準問題を使って実験をし、C4.5、単純ベイズ、ラッパーベースなどの特徴選択法と比較した。
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More Less | | Years of translation experience: 3. Registered at ProZ.com: Mar 2009. Became a member: Apr 2009. | | N/A | English to Japanese (TOEIC 905)
| | N/A | | Microsoft Excel, Microsoft Word, SDL TRADOS | | CV available upon request | | About me
Michitaka Suzuki Ph.D. (Doctor of Science)
Yokohama Japan
Received Doctor of Science degree in physics from Tohoku University in 1987.
Worked for the Computer College of Iwasaki Gakuen from 1985 to 1991.
Worked for EXA Corp., IT company, from 1991 to 2005.
Published several scientific papers in English.
Translated software manuals, scientific papers, patent documents, etc.
Specialize in
Computers, IT, Engineering, Science, Mathematics & Statistics, Physics, etc. | This user has earned KudoZ points by helping other translators with PRO-level terms. Click point total(s) to see term translations provided.
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Keywords: technology engineering science medical mathmatics statistics PhD
Profile last updated
Sep 14 |
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